How to Cache vLLM Model in FastAPI for Faster Inference

2026-03-24 · Source: Andrej Baranovskij · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This content describes an effective method for running inference with Vision Language Models (VLMs) and Large Language Models (LLMs) using a FastAPI application on Nvidia GPUs, specifically highlighting the Sparrow framework. The core challenge addressed is the significant model initialization time (around 40 seconds for models like Mistral small 3.2 24B on an RTX 6000 with 96GB RAM) versus much faster subsequent inference. The proposed solution involves caching the loaded model within the FastAPI application's global scope, ensuring the model is loaded only once and reused for subsequent inference requests. This approach, implemented in Sparrow and available on GitHub, has proven stable in production, avoiding repeated model loading and significantly improving inference efficiency for VLM backends on Linux.

Key takeaway

For MLOps Engineers deploying VLM or LLM inference services, implementing a global model cache within your FastAPI application is crucial. This strategy, demonstrated with VLM on Linux, drastically cuts down on model initialization overhead, ensuring that your inference endpoints respond quickly and efficiently. You should integrate a caching mechanism to load models once at application startup or on the first request, then reuse them for all subsequent calls.

Key insights

Caching VLM/LLM models in FastAPI's global scope significantly reduces inference latency by avoiding repeated initialization.

Principles

• Load models once, reuse often.
• Separate initialization from inference.
• Global cache for shared model access.

Method

Initialize VLM models once into a global FastAPI application cache. On subsequent requests, retrieve the model from cache instead of reloading, ensuring efficient inference.

In practice

• Implement model caching in FastAPI.
• Use VLM backend for GPU inference.
• Consider parallel model loading.

Topics

• VLM Inference
• FastAPI
• Model Caching
• LLM Deployment
• GPU Acceleration

Best for: Machine Learning Engineer, MLOps Engineer, Software Engineer

Related on AIssential

• Semantic Caching for LLMs: FastAPI, Redis, and Embeddings — Semantic caching reduces LLM costs and latency by reusing responses for semantically similar queries via a layered approach.
• A Coding Implementation on kvcached for Elastic KV Cache Memory, Bursty LLM Serving, and Multi-Model GPU Sharing — kvcached dynamically allocates KV-cache memory, optimizing GPU utilization for bursty LLM workloads.
• I Deployed Local LLMs in Production for a Year. Part 1: The Mental Model — Production LLM deployment requires understanding hidden queues, KV cache memory, and multi-phase model loading beyond basic tutorials.
• Accelerating LLM Inference with Prompt Caching for Open‑Source Models on Databricks — Prompt caching for LLMs significantly reduces inference latency and increases throughput by reusing identical prompt prefixes.
• Running Multiple Models on One GPU with vLLM and GPU Memory Utilization — vLLM's `gpu_memory_utilization` parameter enables efficient multi-model inference on a single GPU by pre-allocating memory.
• Optimize, deploy, and benchmark an open-source LLM with vLLM — Efficient LLM inference is achieved through specialized memory management and serving systems like vLLM.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Andrej Baranovskij.